UPDATE 2 (July 15, 2011): This update clarifies that my comments about Wyatt et al (2011) pertained only to an illustration in the poster and not to the paper itself. The illustration in the poster does not appear in the paper. And the update also provides links to the comments by Marcia Wyatt (the lead author of the paper) on the cross post at WattsUpWithThat. This update begins after Figure 6, under the heading of “WYATT ET AL (2011)”.

The AMO+PDO curve has been compared to a number of surface temperature variables. Unfortunately, the AMO and PDO datasets cannot be summed.

THE AMO IS DETRENDED SST ANOMALY DATA, BUT THE PDO IS NOT

The AMO data through the NOAA Earth System Research Laboratory (ESRL) AMO webpageis detrended North Atlantic Sea Surface Temperature (SST) Anomalies. (For those who would like an explanation of detrending, refer to the discussion of Figure 6 below.) The PDO, on the other hand, is the product of a principal component analysis of detrended North Pacific SST anomalies, north of 20N. Basically, the PDO represents the spatial patterns of the North Pacific SST anomalies that are similar to those created by El Niño and La Niña events. Since the responses of the North Pacific SST anomalies to El Niño and La Niña events are also impacted by Sea Level Pressure, the PDO and El Niño-Southern Oscillation (ENSO) proxies like NINO3.4 SST anomalies can differ at times.

If one were to detrend the SST anomalies of the North Pacific, north of 20N (the same method used to create the AMO data), standardize it, and compare it to the PDO, the two curves (smoothed with an 11-year filter) appear to be inversely related, Figure 2.

Figure 2

In fact, if we invert the PDO data, multiply it by -1, Figure 3, we can see that they are inversely related and that the detrended North Pacific SST anomalies lead the inverted PDO data for much of the time. That inverse relationship indicates that, over decadal time periods, when the PDO is rising, the detrended SST anomalies are falling and vice versa.

Figure 3

In short, the PDO is an abstract form of North Pacific Sea Surface Temperature data that does not represent the Sea Surface Temperature of the North Pacific. For that reason, it cannot be used to determine the impact of the North Pacific SST on Global Temperatures.

THE “AMO+PDO” DATA AND ITS COMPONENTS

There’s another curious thing about the “AMO+PDO” dataset that can be seen if we plot it along with the AMO and the PDO data used to create it. Refer to Figure 4. Notice how the AMO minimum in the early 1900s is much lower than the minimum in the 1970s. It should not be if the North Atlantic SST anomalies have been detrended.

Figure 4

Figure 5 shows the current AMO data from the NOAA/ESRL website smoothed with an 11-year filter. The early 20thCentury minimum should be comparable to the 1970s minimum.

Figure 5

THE AMO DATA USED IN THE “AMO+PDO” SPREADSHEET IS ACTUALLY NORTH ATLANTIC SST DATA

As noted earlier, the NOAA Earth System Research Laboratory (ESRL) calculates the Atlantic Multidecadal Oscillation (AMO) data by detrending North Atlantic SST anomalies. They use the coordinates of 0-70N, 80W-0 for the North Atlantic. The current version of the ESRL AMO data can be found at ESRL : PSD : Download Climate Timeseries: AMO SST. It’s identified on the webpage as the “AMO (Atlantic Multidecadal Oscillation) Index”.

The AMO data used in the “AMO+PDO” spreadsheet and graphs has not been detrended. In other words, it’s “raw” North Atlantic SST anomaly data. The NOAA ESRL/PSD appears to have changed how they present AMO data sometime between 2007/08 and now. They note on the Atlantic Multi-decadal Oscillation portion of their Climate Indices webpage, “this index is newly computed from a new dataset. Please use it and note that it supersedes the old indices. The data is calculated from the Kalplan SST. See the AMO webpagefor more details.” Or the ESRL had two AMO datasets available online back in 2007/08.

The older version of the ESRL/PSD AMO data used in the AMO+PDO dataset (through 2006) is still available online: AMO(unsmoothed): Standard PSD Format. It’s linked to the AMO – NOAA Earth System Research Laboratorywebpage. They list the source as “Calculated from the HadISST1.” That’s wrong. The linked data is based on Kaplan SST data, not HADISST. They also note that the data is “Area averaged SST in the Atlantic north of 0”. There’s no mention of detrending.

A NOTE ABOUT DETRENDING

For those who are unsure what I’ve meant by detrending, refer to Figure 6. It’s a graph borrowed from my post An Introduction To ENSO, AMO, and PDO — Part 2.The trend of the SST anomalies is determined, and the trend values are subtracted from the SST anomaly data, “flattening” the trend.

Figure 6

WYATT ET AL (2011)

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UPDATE 2 (July 15, 2011): The following discusses an illustration from the poster for Wyatt et al (2011). In an earlier update that I placed at the end of this post, I had noted that the illustration from the poster does not appear in the paper, but since that update is at the end of the post, many readers may have missed the clarification. With this update, I wanted to reinforce that my comments are not about the Wyatt et al (2011) paper; they are about an illustration from the poster that did not appear in the paper.

I was asked to comment on the Wyatt et al (2011) AMO+PDO graph included in their poster, Figure 7. Unfortunately, there’s very little discussion of the graph on the poster and there’s a paywall on the paper, so I have no means of verifying the sources of the data. But…

Figure 7

The same basic problems (the PDO does not represent the SST anomalies of the North Pacific and the PDO is inversely related to the detrended SST anomalies of the North Pacific) apply to the Wyatt et al (2011) AMO+PDO graph.

In addition to that, the note in the poster that “the NH [Northern Hemisphere] surface temperature time series can be nearly perfectly represented as the weighted sum of the AMO and PDO reconstructions” raises a red flag for me. Nearly perfectly? There are significant differences between SST datasets. The Sea Surface Temperature dataset that’s part of the combined surface temperature dataset must be used if a nearly perfect fit is to have any meaning. That is, for example, when referring to the Hadley Centre’s HADCRUT Land+Sea Surface Temperature data, AMO and PDO data based on HADSST2 should be used. Since there’s the paywall on the paper, I can’t confirm if Wyatt et al used the related SST data to create their AMO and PDO data, so the following portion of this discussion (Figures 8 and 9) is for example only.

For AMO data, Wyatt et al refer to Enfield (2001), which used detrended Kaplan SST anomaly data for the North Atlantic. But there are no surface temperature datasets that use Kaplan SST. (This was one of the problems that Tamino had encountered in his AMO post.) ERSST.v3b data is used by NCDC. GISS uses HADISST and Reynolds OI.v2 SST data for their combined surface temperature products. And HADSST2 is used in the Hadley Centre’s HADCRUT. And the differences between Kaplan and the three SST datasets used in Surface Temperature data can be significant, as shown in Figure 8. (For those wondering why the AMO minimums in the 1970s is lower than the early 20thCentury minimums in this graph, I’ve detrended the data starting in 1900.)

Figure 8

If the PDO could be combined with the AMO data (it can’t), using the correct PDO dataset would also be important. Unfortunately, the PDO data was not referenced in the Wyatt et al poster or their guest post at Roger Pielke Sr’s blog. The most-often-used PDO dataset referred to and used in climate studies is the one available through the JISAO website. In fact, Marcia Wyatt’s co-authors Tsonis and Kravtsov referred to the JISAO PDO data in their 2007 paper A new dynamical mechanism for major climate shifts. But, the JISAO PDO data is based on two obsolete SST datasets (UKMO and Reynolds OI.v1) from 1900 to 2001. None of the current surface temperature datasets use UKMO SST or the obsolete Reynolds OI.v1 SST data. And there are again significant differences between the JISAO PDO data and the 1stPrincipal Components of the detrended North Pacific SST data used in the Surface Temperature products, Figure 9.

Figure 9

The other curiosity about the Wyatt et al “AMO+PDO” graph is the weighting: the AMO is multiplied by 0.83 and the PDO by 0.44. The surface area of the North Pacific (20N-65N, 100E-100W) used for the PDO is slightly larger than the North Atlantic (0-70N, 80W-0) surface area used in the AMO. Based on the surface areas, one would expect a weighting of 53% Pacific and 47% Atlantic, or some similarly weighted factors. Again, since the paper is paywalled, I have no idea how Wyatt et al explain the graph, its components or the weighting.

Wyatt et al could replace the PDO data in their AMO+PDO graph with an SST-based ENSO index like NINO3.4 or Cold Tongue Index (CTI) SST anomalies and wind up with similar results. An AMO+ENSO Proxy curve may not fit nearly perfectly with the Northern Hemisphere Temperature anomalies, but that combination should better represent the two indices that impact Northern Hemisphere surface temperature anomalies.

CLOSING QUESTION

The Pacific Decadal Oscillation is a well-established climate index that’s used for many variables other than surface temperature. Unfortunately, many people mistakenly believe it is calculated the same as (and is therefore comparable to) the Atlantic Multidecadal Oscillation. Do we need a new index to represent the multidecadal variability of North Pacific Sea Surface Temperatures? The amplitude of the multidecadal variations in detrended North Pacific SST anomalies is less than the variations in the North Atlantic, Figure 10. And the frequencies are somewhat different, meaning the two datasets can run in and out of synch.

Figure 10

UPDATE (June 8, 2011)

Here’s a curiosity: I just checked all of the illustrations for Wyatt et al (2011). Figure 2 from the poster, which is Figure 7 in this post, the graph that includes the weighted AMO+PDO dataset, does not appear in the paper. I double checked by having Adobe Acrobat do a word search and the phrases “weighted sum” and “AMO and PDO reconstructions” do not appear in the paper.

And for those interested, Wyatt et al used the ESRL AMO data, the JISAO PDO data, and HADCRUT NH surface temperature data.

So as shown the are not directly additive. But if we could come up with another term that could put them on the same basis such as dPDO (dT/dPDO) = dT for a given change of PDO in a given region and then the same for AMO maybe then they could be added. This could be viewed for whole world, North America, Eastern North America, etc. Maybe someone could coax the numbers from as far back as one can go.

I would think that a new PDO* would be useful, simply because it would be clean, and comparable to the AMO, not that they should be combined, just that having the equivalent information for the Pacific is necessary to get the fill picture. As you show in figure 10, there is information there, even if there is a lead or a lag effect.

From my above comment on dTs, Another thought is that the data might show a crossover effect like mutual inductance in a transformer circuit. Lets call it mutual north american oscillation where the PDO and AMO are multiplied together with mutual coefficient as an additional term.

If AMO is simply detrended SST then what support is there for calling it an “oscillation?” Shall I detrend the GISS US chart and call it the “US Multidecadal Oscillation?” just because I don’t know what caused the sine wave but wish to discount AGW claims? Can more cycles be reconstructed further back than a century?

Since there’s the paywall on the paper, I can’t confirm if Wyatt et al used the related SST data to create their AMO and PDO data, so the following portion of this discussion (Figures 8 and 9) is for example only.

Why not just pay the 35 dollars and make this a complete article and not leave suppositions in place? It seems a minor expense to complete an article for thousands or tens of thousands of readers. I’m sure someone here would donate and Anthony could pass it along. Seriously, all this hand-wringing over paywalls instead of just paying for the real item in order to perform a proper critique seems petty. I know no one here gets paid, but c’mon, 35 dollars is going to stop you from making sure?

This is widespread problem in this community, and the solution is not necessarily to scream for open science. It is not a big deal to occasionally take out your credit card to help you with your work, even unpaid work.

I’ll have to read this closer tonight, as Joe D’Aleo’s AMO+PDO article was one of the things that made me an active skeptic. (Let’s see, I guess I was waiting SC23 to end and for the PDO to go negative. Joe pointed out that the Sun couldn’t get much less active and that the PDO had gone negative and BTW, the PDO correlates with temperature better than CO2 and temperature.

Then Leif comes along and messes up my simple concept of solar activity and climate, and now this! Actually, it occurred to me a while back that if AMO data is largely temperature anomaly data, then a correlation shouldn’t be much of a surprise if sea water temps affects air temp.

Fortunately, there’s a lot of other factors to be studied from temperature data quality to Livingston and Penn’s warming and fading sunspots. Does it make sense to look at non-detrended Pacific/Atlantic data and compare that to non-detrended temperature data?

Unless the data is measured with error, you never, ever, for no reason, under no threat, SMOOTH the series! And if for some bizarre reason you do smooth it, you absolutely on pain of death do NOT use the smoothed series as input for other analyses!

I want to stress that if D&E did not smooth their data, the correlation would not have been as high; but as high as it would have been, it would still have been expected. All that smoothing has done here is artificially inflated the confidence D&E have in their results. It does not change the fact that AMO + PDO is well correlated with air temperature.

jeez says: “Why not just pay the 35 dollars and make this a complete article and not leave suppositions in place? It seems a minor expense to complete an article for thousands or tens of thousands of readers.”

Let me turn your question around, jeez. Since the AMO and PDO can’t be combined, why waste $35 to confirm which datasets they used? As I noted, I provided the graphs and discussion as a reference–to show that there are significant differences between SST datasets.

jeez says: “Why not just pay the 35 dollars and make this a complete article and not leave suppositions in place? It seems a minor expense to complete an article for thousands or tens of thousands of readers.”

Perhaps because this is only one of many 35 dollars that need to be spent and the big oil money has not filtered down yet?

On the other hand YOU could pay the 35 dollars and then write a comment here that fills in the gap.

Its also worth pointing out that linear de-trending over century periods is of limited use if forcings were not linearly changing over the period in question, at least if you are trying to extract the unforced variability.

You can, indeed, form a model from two discrete datasets that are not, themselves, directly comparable or ‘addable’.

Particularly when you’re flailing around with the explicit goal of an empirical model.

If I’m studying ‘failure modes of Joe’s bookcases’ through the question “How much weight can it support?” and decide the two crucial variables are “What day of the week was it constructed on?” and “Did it get wet?”, I can, arrive at a functional predictive model. Without having the foggiest notion – or even a completely erroneous notion – of what the underlying physics or chemistry is – pretty much identically to how pre-Kepler/Copernicus models -did- actually do a decent job of predicting the locations of the planets. While being fundamentally wrong.

You can’t “add” days of the week to wetness – they’re literally completely different things. The -reasoning- behind some of the items in an empirical model might be completely hidden, like perhaps Joe buys his wood in different places on different days of the week.

The crucial test of an empirical model is through predictive tests on unexposed data.

Sarcasm for the education of young researchers:
Here’s the plan: Have a person go through a city’s west side neighborhoods and record the age in years of all the autos visible from the streets. Then the mean age is calculated. Have another person go to the east side neighborhoods and measures all the people and calculate mean height in feet. Now add the mean age and the mean height and compare this number to the business bankruptcies in the city in the previous year. After many years — predicting bankruptcies will be a piece of cake. S/off

Ric Werme (June 8, 2011 at 1:30 pm)
“Does it make sense to look at non-detrended Pacific/Atlantic data and compare that to non-detrended temperature data?”

Finally, common sense. [Note: Not all definitions of AMO include detrending. It’s easy to make a strong case against linear detrending.]

–
Bob, there’s much to discuss, but before we get started, we really do need the comparative graph of North Pacific SST across different data sources to put things in the same perspective as your AMO framing.

Since there’s the paywall on the paper, I can’t confirm if Wyatt et al used the related SST data to create their AMO and PDO data, so the following portion of this discussion (Figures 8 and 9) is for example only.

Why not just pay the 35 dollars and make this a complete article and not leave suppositions in place? It seems a minor expense …

I case you didn’t realize it we’re not just talking about $35. This is just one example of a $35 paper for sale. From following the links in WUWT, I have encountered at least 10 or 15 pay-walled papers in the past month. Would I like to be able to read these articles, you betcha! But that would have cost me between $350 to $525. And there is still the economic downturn to contend with. Plus [self imposed long snip] expenses!

Since you seem to be so free with other peoples’ money, I can only assume that you are a liberal and have never had to manage your own limited funds.

jeez, prove me wrong by offering to pay for all of us to have access to these papers!

Here’s a curiosity: I just checked all of the illustrations for Wyatt et al (2011). Figure 2 from the poster, which is Figure 7 in this post, that includes the weighted AMO+PDO dataset does not appear in the paper. I double checked by having Adobe Acrobat do a word search and the phrases “weighted sum” and “AMO and PDO reconstructions” do not appear in the paper.

Bob brings up fair points. But perhaps I can offer more clarity on the WKT paper.

The figure to which Bob refers – showing NHT regressed onto predictors AMO and PDO – was used in the posters, not in the paper. A number of weighted combinations of reconstructed components of the “stadium wave” climate signal could be combined in a variety of proportions to yield similar results. This does not diminish the observation that the trend of combined AMO and PDO values aligns closely with that of NHT.

It is important to note that reconstructed components (RCs) of this multivariate data set, generated through multi-channel singular spectrum analysis (M-SSA), represent the climate signal shared by all indices involved; these RCs represent a pair of leading modes of climate variability that propagates through the set of indices. The modes, themselves, represent co-variability among the indices of the data set. The M-SSA method applied here does not involve isolating each index and smoothing it. Instead, a collection of indices are considered together. In this specific application, M-SSA identifies the signal shared among all indices. M-SSA is a type of EOF analysis. EOF analysis identifies spatial patterns that co-occur at a zero-lag. On the other hand, M-SSA identifies spatial patterns that co-occur at non-zero lags – in other words, it is useful for identifying propagating signals of spatio-temporal variability.

Normalized RCs of AMO and PDO have a high fractional variance of the signal identified in this study. The combined influences of the signals in AMO and PDO can be seen in the NHT, which also possesses a high fractional variance of the signal.

The paper makes no conclusion on global warming; the conclusion is solely regarding a multidecadally varying climate signal shared among a collection of indices, a signal that propagates across the hemisphere. A warm (cool) Atlantic triggers a cascade of polarity changes in participating teleconnections, resulting in a cooling (warming) hemispheric climate signal about 30 years later – the “stadium wave”. The periodicity of changes in the North Atlantic AMO appears to be largely governed by the Atlantic sector of the meridional overturning circulation (AMOC). As the cascade of atmospheric and lagged oceanic teleconnections converts a warm (cool)-Atlantic-born signal into a Pacific cooling (warming) signal, the AMOC is re-configuring the Atlantic SST signature. By the time the Pacific begins to cool (warm) as a result of an initially warm (cool) North Atlantic, the North Atlantic, itself, is cooling (warming). The conflated result of temperature profiles within each oceanic basin is a cooling (warming) hemisphere, poised to reverse trend as a result of the once-again-cooling (warming) North Atlantic SSTs (which will ultimately lead to a warming (cooling) climate). No conclusion on what exactly causes the NHT is given, just that it strongly coincides with the trends of combined PDO and AMO. At the paper’s end, a lengthy discussion is devoted to possible mechanisms underlying the stadium-wave dynamics, based on numerous model-based and observational studies.

While the article does not address global warming, other than to underscore the importance of discriminating between climate-change that is natural and that which is of anthropogenic origin, this article’s findings may be misconstrued to support one view over another. To interpret it in this way is to loose a very powerful message: that multidecadal climate variability, whose pace of variability is strongly governed by the AMOC, appears within an interconnected network of climate indices, the interaction of which results in a negative-feedback cascade of teleconnections. The study covers only the 20th century – the rigorous statistical methods used making it unlikely that this low-frequency alignment would appear in various regional climate time series purely at random. The authors suggest future study will address evidence of the stadium wave in past climate and in modeled scenarios.

Wyatt, Kravtsov, & Tsonis (2011) is NOT about PDO+AMO, but there’s plenty in the paper to stimulate discussion about spatiotemporal phasing. I suggest that everyone involved in online climate discussions obtain a copy of the paper as it provides a useful starting framework for raising the level of discussion of multidecadal oscillations. Perhaps some will write to the university and ask them to pay the fees to have the paper put in the public domain.

To interpret it in this way is to loose a very powerful message: that multidecadal climate variability, whose pace of variability is strongly governed by the AMOC, appears within an interconnected network of climate indices, the interaction of which results in a negative-feedback cascade of teleconnections.

Indeed. The detection of teleconnections is after all what “global climate science” is attempting to do. Noting existence is the first step to winkling out the nature of the linkage.

The PDO is a pattern. And it does not actually match the climate cycles if you use the monthly values (only a little match if you use a 25 year moving average). How can you add a numerical representation of a pattern to actual SST numbers in another region.

I also agree that we need a new PDO index to reflect the north Pacific’s impact on the climate. It should not be a pattern. It should be ocean SSTs in a particular region, the one that has the most impact. It should be about how those ocean SSTs impact temperatures, clouds and water vapour. The things that actually impact the climate.

The driver of the PDO pattern is really the ENSO. The PDO pattern is just an after-affect of the ENSO.

So why not just use the ENSO for which there is already very strong, well-documented correlations to every major climate indices there is .

If you want to add in a little bit of extra lag (which looks like might be needed sometimes) you could add in the Kuroshio current as well. The Kuroshio is the north Pacific’s version of the Gulf Stream. It is just as strong. And it lags somewhere around 6 months behind the ENSO. The equatorial Pacific surface currents actually leak off north-west into the Kuroshio.

Marcia Wyatt: Thanks for the detailed overview of the paper. You wrote, “A number of weighted combinations of reconstructed components of the “stadium wave” climate signal could be combined in a variety of proportions to yield similar results. This does not diminish the observation that the trend of combined AMO and PDO values aligns closely with that of NHT.”

The majority of the alignment occurs between the AMO and the Northern Hemisphere Temperature data. The PDO is a secondary “overlay”, for lack of a better word, that helps to tune the alignment. As noted in my post, since the detrended SST anomalies of the North Pacific are inversely related to the PDO, the PDO appears to be the wrong dataset to use in that graph.

Here’s a comparison of a weighted sum of the AMO+PDO and a weighted sum of the AMO+NINO3.4 SST Anomalies (all data standardized) using the same weighting you used in the poster:
There’s very little difference between the curves, but the NINO3.4 data are SST anomalies and known to impact global surface temperatures, where the PDO is not known to impact global temperatures, since it’s basically an aftereffect of ENSO.

I received the sort of replies I was expecting. I hesitate to play a game of one-up-manship, but it is unfortunately called for.

I have donated to this site multiple times.

I offered to cover Anthony’s plane flight to the NCDC in 2008, but he declined.

I am in debt and have been for many years. This does not stop me from donating money or hundreds of hours to items of importance. I have a blue collar job.

I often try to get this community to step up its game, to become more solid, to be stronger, to produce better material, to be more credible. The quote I noted above was singularly unimpressive for an article written for thousands of readers. It demonstrated a lack of commitment. I called it out.

The person who above who decided to identify me as a “liberal”, (I think he means limousine liberal to be exact), demonstrates the sloppy, small-minded, thinking and knee jerk political reaction I abhor, and which greatly and I do mean greatly, diminishes the credibility of this community. You guys can do better. I want you to do better.

Bob, perhaps it doesn’t matter whether you know exactly what they used, however, the way you wrote the paragraph I quoted made it sound as if it did and gave the impression that you ran to within a foot of the goal, then wandered off for want of the price of a round of drinks.

Having noted the deficiencies noted above about the PDO, the ocean cycles explain more than half of the global temperature changes (and this happens on as little as a monthly scale – we do not need to use smoothing functions and 20 year moving averages – the global climate is impacted within a week to as much as 3 months from changes in the ocean cycles).

After accounting for the changes, there is a still a warming trend left-over which could be described as close to a linear 0.053C per decade (less than half of that predicted by global warming theory) or, more accurately, it is function of CO2 or the climate forcings as Zeke mentioned above (again it is still less than half of that predicted).

Having said that, it does look like this paper might be quite interesting. Wyatt appears to have used all kinds of different ocean cycle comparisons. We will have to wait until a free version shows up. It is taking less and less time now for pay-walled papers to be put up on the internet (I’ve been using the “QuickView” function in Google lately which is getting by many pay-walls – I’m not sure if they are aware of this – but not for this one so far).

You asked, “Bob, do you have any comparative insights to share on North Pacific data quality vs. that of North Atlantic.”

The SST sampling is more complete for longer in the North Atlantic than the North Pacific, so there’s much less infilling in the North Atlantic. (Assuming the data set is infilled. HADSST2 is not infilled, other than taking data in 2 deg grids and expanding them to 5 deg grids) Since there’s more infilling during the early 20th century in the Nrth Pacific, we’re relying on the infilling methods used by NOAA and Hadley Centre, and whether the data producers reinserts the data back into the infilled fields. Hadley reinserts the data in HADISST; NOAA does not reinsert for ERSST.v3b.

You asked, “And do you have anything to say about supposed AMOC equivalence to AMO?”

My recall of this is that there are not enough AMOC observations to confirm this, so they’ve had to rely on models to confirm it.

There appears to be evidence that oceanic parameters, in both the Atlantic and Pacific regions, influence climate on interdecadal timescales. Can we frame indices which can reflect that in a scientifically rigorous manner and if so, what do they tell us?

What is the accuracy and confidence level of the PDO+AMO vs actual historical temperature data? If the accuracy is above 90% and the confidence level is +/- 0.1 degree C, we should use it!

Just because you currently don’t understand why it works, or think you know why it shouldn’t work, does not negate the value of the tool. In 18th century England, a very astute man found that grain prices varied inversely with sun spot numbers. He didn’t know why, but he was still able to benefit from that data! So should we, especially if it helps us prevent the destruction of our economies by the Eco-terrorists!

On an instinctive primal level, many react in visceral disgust to the ethically acidic paywalls that separate the general public from information used to support public policy. You cannot defeat such primal instincts with argument. The force which you oppose is that of nature.

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lgl, be sure to extend your exploration to NPI if you haven’t already:

–Rhys Jaggar (June 9, 2011 at 6:04 am)
“There appears to be evidence that oceanic parameters, in both the Atlantic and Pacific regions, influence climate on interdecadal timescales. Can we frame indices which can reflect that in a scientifically rigorous manner and if so, what do they tell us?”

It’s not just the North Pacific & North Atlantic, but rather western boundary currents. Bob has done an excellent job of raising awareness of this by drawing attention to KOE & SPCZ. The Gulf Stream undeservedly gets the lion’s share of attention.

Continental areas have higher-amplitude variations than maritime ones. It’s continent-ocean boundaries that demand our attention. The North Atlantic is a smaller basin and it has (therefore not surprisingly) higher-amplitude variations that correlate interannually more strongly with global surface temperatures than do ENSO variations – (many people asleep at the wheel, not seeing this).

The pattern is a GLOBAL one – (see the writings of Tomas Milanovic at Dr. Judith Curry’s blog Climate Etc.) – of WESTERN BOUNDARY CURRENTS in general. The dominating “teleconnections” operate on semi-annual to annual timescales AT MOST. Notions of long lags are NONSENSICAL MISCONCEPTIONS.

For a refreshingly sensible perspective, see Bill Illis’ comments (June 9, 2011 at 5:53 am) on the timescale of spatial phasing – (“the global climate is impacted within a week to as much as 3 months from changes in the ocean cycles”).

The community’s resistance to understanding Bill on this point puts a HARD CONSTRAINT on the potential for discussion advancement.

Fantasies about AMOC and multidecadal ocean currents with mythical amplitudes & heat capacities ORDERS OF MAGNITUDE HIGHER THAN OBSERVED should be abandoned in favor of OBSERVATION-BASED focus on western boundary currents and north-south maritime-continent contrasts.

I will consider writing an article on interannual AMO, SOI, & global surface temperatures to help overcome some of the widespread paralyzing misconceptions.

Bill Yarber says: “What is the accuracy and confidence level of the PDO+AMO vs actual historical temperature data? If the accuracy is above 90% and the confidence level is +/- 0.1 degree C, we should use it!”

Those are questions you’d have to ask someone who is attempting to promote the use of the datasets.

You continued, “Just because you currently don’t understand why it works, or think you know why it shouldn’t work, does not negate the value of the tool.”

I’ve explained and illustrated in detail in this post and in others why I understand that it does not work. One can either accept what the data shows or one can elect not to. In an earlier comment, Marcia Wyatt noted that one could use many combinations of other variables in place of the PDO data and get similar results in that comparison graph. That confirms the AMO is the primary source of the long-term argreement between the Northern Hemisphere temperatures and the combined climate indices. That’s not news. There are numerous papers that show the AMO is resposible for much of the multidecadal variations in Northern Hemisphere surface temperature.

Minor problem with a new PDO…the PDO is not spatially similar to the AMO. The PDO is an oscialltion with significant logitudinal shape…a warm PDO means the tropical Pacific AND EASTERN RETURN CURRENTS are above normal. Your finding that the PDO index and NPAC SST are inversely related is not remotely surprising to anyone who knows what the PDO actually looks like. A warm PDO implies COLD waters in the Gulf of Alaska and warm around that cold bubble.

And it does NOT follow that a cold bubble in the NPAC should produce cold planetary temperatures. I think you’re actually missing the point when it comes to combining the PDO’s influence on temperature with the AMO’s influence. The theory goes that a warm PDO comes accompanied with increased low cloud cover in the NPAC (insulating the polar regions from heat loss to space while simultaneously cooling the NPAC). The AMO is just a region-wide warming on a multi-decadal scale…it has no longitudinal shape…its’ influence on planetary temperatures is obvious and directly linear. The two indices are not the same…they probably shouldn’t be combined directly..but it does not follow that if a warm AMO = increased temps a cold bubble in the NPAC should cause decreased temps.

The necessary analysis would be to determine the non-linear relationship between planetary temperature and each index while holding the other index constant. Multilinear regression is the tool used by Wyatt…that’s why the different weights (you even note the exact reason…the PDO is a smaller scale oscillation…a weaker signal caused by spatial differences that don’t exist in the AMO…it would seem obvious to someone acquainted with statistics that a weaker signal will not correlate as well with a big term like planetary temperatures.

I recommend a simple test to ease the confusion here…I agree that you can’t just add the two indices together…but you can reduce each index to a unitless trending term (making detrending irrelevant BTW), and produce much the same graph that Wyatt did…it was be dAMO/dt * k1 + dPDO/dt * k2 = dTep/dt, but the fit will be just as strong.

This isn’t satisfactory to a real scientist…we need a dynamical explanation for any statistical fit…but there would be nothing wrong with that analysis as something to raise questions for future research into those dynamical explanations.

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jeez, if I have misjudged you, I apologize. I have reread your first post and can see what you were trying to say. However, to me it still does read a bit cavalier with regard to Bob Tindale’s money.

I do admit that my response to your comment was focused solely on that seemingly lack of concern. This is kind of a sore point for me, as I am also in debt and will probably be so until I die. (My life insurance should be adequate to pay the debt and not leave it for my kids.) The “limousine liberals” (that is indeed what I meant) that I encounter daily have a never ending list of “good works” that need funding out of my slim wallet.

I can see your perspective and I do have to give you credit for your generosity. If your accounting is accurate (and I assume that the moderators have verified that), you are obviously a very caring person. I hope that you can understand my position as well as your own.

Bob explained perfectly (& his notes on ADO & PMO are certainly well-worth a look for other readers) …but I noticed that you were left musing about how to interpret some of the summaries — probably a welcome sign that Bob has succeeded in encouraging a conceptualization makeover.

But why PDO when NHT is LITERALLY a SIMPLE function of BOTH North Pacific & North Atlantic SST? …so the correlation with NHT SHOULD be a no-brainer, but for whatever mysterious reason, it isn’t for a lot of commenters.

PDO has utility, but the problem Bob is tackling is that many of the people tossing the term around (a) lack the foundations to interpret factor analyses (such as PCA, upon which PCA is based) &/or (b) don’t know the difference between PDO & North Pacific SST.

Apparently (according to WUWT commenter A G Foster) NASA’s Richard Gross is now working on what Sidorenkov discusses in section 3 (“Nature of the decades-long variations in the Earth’s rotation”):

I disagree that the MOST serious problem limiting research progress (at present) is lack of physical understanding; rather it is insufficiently thorough data exploration prior to modeling. The modelers are off to the races modeling before they’ve even applied the prerequisite patience NECESSARY to understand what it is they’re supposed to be modeling. This is a deeply rooted cultural problem and it has not only infected but also severely compromised our funding strategies. Societal “reasoning” systems are ill; the condition from which they suffer is models based on UNTENABLE assumptions. (Grass roots are working on a cure.)

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Marcia, thanks for stopping by to volunteer. Looking forward to future exchanges on your stimulating ideas, if ever & whenever you have time & inclination.

I am a Ph.D. candidate in atmospheric sciences studying wave interactions in the atmosphere and their implications to the predictability of atmospheric disturbances in the medium range (3-14 day) forecasts from numerical weather prediction models…

And even I cannot understand what it is you’re saying about length-of-day variations, solar cycle influences on global atmospheric annular momentum, or the interactions between the Earth, Moon and Sun and specifically the implications to climate dynamics. You are speaking Swahili and I’m speaking (evidently” the language of DERP.

Please…we need a post from you where in you discuss absolutely ZERO complex wavelet transform theory and focus entirely on the PHYSICAL MEANING of your argument and how it applies to climate change. My brain just exploded trying to read a post of yours you linked in your reply to me. Seriously, Paul…OUCH.

Bob Tisdale says (June 9, 2011 9:18am): “…Marcia Wyatt noted that one could use many combinations of other variables in place of the PDO data and get similar results in that comparison graph. That confirms the AMO is the primary source of the long-term agreement between the Northern Hemisphere temperatures and the combined climate indices. That’s not news. There are numerous papers that show the AMO is responsible for much of the multidecadal variations in Northern Hemisphere surface temperature.”

Bob, you are, without doubt, correct. An AMO-relationship with multidecadal variations in NHT is not news. But while we find a strong co-variability between AMO and NHT (with NHT leading AMO by ~4 years), and do not doubt a relationship between NHT and the SST-characteristics defining the AMO, we are not stating what that exact relationship is. Generation of the NHT signature is clearly complex.

If our results and interpretation of results are correct, what is news is the propagation of a climate signal throughout the Northern Hemisphere – a sequence of atmospheric and lagged oceanic teleconnections, resulting in an NHT trend oppositely signed from the initiating source. Such network “communication” is pervasive throughout natural and manmade systems. Finding it in climate leaves one to wonder, why not? The climate signal accounts for a substantial fraction of variance in several indices: NHT, AMO, PDO, and AT and only a small fraction of variance of NINO3.4 and NAO. Despite the latter mentioned indices possessing small fractional variances, in network theory, even the weakest links have profound effects on transforming “small-world” regional/local behavior into “global” behavior. Our approach is simply an additional way to view the climate – a complement to the reductionist strategy that focuses on the “parts”. We attempt to see the parts assembled.

Careful Brian. That estimate (4 year lag) is temporally-global (not to be confused with spatially-global). There’s not really a lag; this estimate simply alerts a careful interpreter to the existence of interannual spatiotemporal variability, as revealed by more temporally-localized methods (such as cross-wavelet methods & multiscale complex correlation).

Marcia, SOI & NAO show the ‘stadium wave’ too if you break the analysis down by season and pay attention to reversals in phase-relations. Also, AIR (All India Rainfall), hurricane frequency indices, & river flows for some regions fit the ‘stadium wave’. A request: It will be helpful if you define AT (and share a link to the data if possible), as that is a variable not yet on the WUWT community radar.

Time-integrated cross-correlation algorithms have utility as initial exploratory tools, but look at the mess Charles Perry (USGS) got himself into by not recognizing the pull of temporally-global extrema on cross-correlation lags. It’s a good place to start, but complex [as in complex numbers, not as in complicated (actually quite simple once one understands)] methods with temporally-local capabilities are needed to avoid nonsensical misinterpretation of lags.

b) “[…] solar activity can affect the radiative equilibrium of the troposphere in an indirect way, which cannot be simply deduced from the magnitude of TSI variations.”

c) “The semi-annual oscillation extends to all latitudes and down to low altitudes, as does the annual term. But, unlike the annual term, the main part of the oscillation is symmetrical about the equator; the partial cancellation of the angular momentum of the two hemispheres, which occurs for the annual oscillation, does not happen there [Lambeck, 1980]. Thus, we have here a measure of the seasonal variation of the total angular momentum of the atmosphere of the two hemispheres at the semi-annual frequency.”

d) “When considering separately monthly averages rather than annual ones, differences in the net radiative flux distribution appear, due to the seasonal variation in insulation which is asymmetric with respect to the equator. Seasonal variations of insulation result in seasonal variations of poleward meridional transport, hence of averaged zonal wind.” [Typo: “insulation” should read “insolation”.]

e) “The argument above serves to show that the semiannual variation in lod is linked to a fundamental feature of climate: the latitudinal distribution and transport of energy and momentum.” [Pole-equator contrast drives pumping.]

f) “The solid Earth behaves as a natural spatial integrator and time filter, which makes it possible to study the evolution of the amplitude of the semi-annual variation in zonal winds over a fifty-year time span. We evidence strong modulation of the amplitude of this lod spectral line by the Schwabe cycle (Figure 1a). This shows that the Sun can (directly or undirectly) influence tropospheric zonal mean-winds over decadal to multi-decadal time scales. Zonal mean-winds constitute an important element of global atmospheric circulation. If the solar cycle can influence zonal mean-winds, then it may affect other features of global climate as well […]“ [Typos: 1) “evidence” should read “observe”. 2) “undirectly” should read “indirectly”.]

“[102] The intermonthly autocorrelations in HadISST1 are weakest in 1910-1945 (not shown). A contributing factor may be the very data-sparse periods 1914-1920 and 1940-1945. Coverage in 1871-1909 was often sparse also, but no year had as few data as 1918 (Figure 3). In addition, the El Nino-Southern Oscillation phenomenon, which engenders strong monthly persistence in the tropics and some extra-tropical regions, was strong and coherent in the late 19th century and generally weak and less coherent between roughly 1920 and 1940 [e.g., Allan et al., 1996].” [my emphasis added]

I took a quick peek at the entries and wanted to reply to those questions directed to me.

Paul Vaughan asked me to clarify the index AT. AT stands for atmospheric-mass transfer. AT relates to large-scale prevailing wind direction over the North Atlantic-Eurasian region. Annual anomalies of predominant wind directionbetween 30 to 80 degrees north are determined from daily examination of pressure maps (cyclonic and anti-cyclonic systems). Positive anomalies reflect a more zonal (easterly/westerly) character; negative ones, a more meridional (northerly/southerly) one. Many of you might be more familiar with the cumulative-sum version of AT, the ACI – Atmospheric Circulation Index (Girs 1974). King et al. 1998 introduced a Pacific-centered analogue to the ACI – the Pacific Circulation Index (PCI) – that reflects integrated behavior of the PDO-related Aleutian Low.

A second point asked was regarding my thoughts on why NHT leads AMO by ~4years. That is the 64,000-dollar question! First, there is no assumption of causality; the observation only indicates lagged covariability. With that said, it is helpful to regard the climate indices – NHT, AMO, AT, NAO, PDO, etc.- as not just the raw climate variables (SSTs, surface Ts, or SLPs) from which the indices are constructed, but rather as a subset or collection of dynamics represented by the climate indices. As an example, recent research suggests AMO is connected to multidecadal variability in frequency of sudden-stratospheric-warmings, which are related to both tropical convective processes and to the integrity of the polar vortex – both features wielding hemispheric influence on the climate. In addition, longitudinal and latitudinal placements of the atmospheric centers-of-action shift with multidecadal variations in AMO, as does the meridional placement of the mean intertropical convergence zone (ITCZ), along with associated changes in Atlantic hurricane activity and in frequency of occurrence of Atlantic-NINOs. Likewise, with multidecadal variability in PDO come changes in placement and strength of atmospheric centers-of-action and in placement and strength of associated oceanic gyres and in meridional mean location of the Pacific ITCZ. These and low-frequency variations in NINO are associated with variations of direction and volume of ocean-flow through the Bering Strait and the Indonesian Through Flow, with cascading influence on the Arctic and Indian Oceans, respectively, thereby influencing salinity and density values of both, with both pathways ultimately influencing the salinity of the Atlantic, which, in turn, influences the vigor of the Meridional Overturning Circulation (MOC). Concomitant phase-related changes in thermocline structure, ocean-heat-flux from the western-boundary currents, and the heat-flux’s effect on overlying storm tracks, in addition to other dynamics (only a handful described here), all play roles in the evolution of NHT.

The list of features associated with these modes and with all the other members within the stadium wave is long. Thus, one can see that the PDO and AMO are not merely interesting or climatologically influential in terms of their SST structure. It is the index-associated dynamics that likely hold the answer to the observed lagged covariance between NHT and AMO.

P.S. I forgot to include the following regarding NHT leading AMO. In our study, we found that the “stadium-wave” climate signal (leading two modes of variability) accounts for a substantial fraction of variance in the Atlantic SST dipole (Keenlyside et al. 2008). MSSA showed the SST-dipole reconstructed index (RC) to be statistically identical to that of the NHT. Both lead a same-signed AMO by about four years. The SST-dipole is considered (albeit not without controversy) to be a proxy for the Atlantic Meridional Overturning Circulation (AMOC).

Paul, I think you quite fairly invited justification of the AMOC-AMO relationship in one of your posts. Our paper discusses this, but a short version follows: As far as controversy linking AMOC to AMO (at a lag), it is true that inconsistent modeling results confuse the issue. It is noteworthy that models with periodicities and boreal-winter atmospheric projections closest to observation seem to require a deep or interactive ocean (ex: Knight et al. 2005; Msadek et al. 2010b). Viewing the matter from a paleoclimate perspective, G. bulloides abundance in the Cariaco Basin is considered to be a proxy for the dipole and for the AMOC (Black et al. 1999) and is supportive of AMO-associated changes co-varying with its abundance. Again, our climate signal accounts for a high fractional variance in the G. bulloides record and its RC coincides with the Atlantic SST dipole and the NHT in our study. Thus, an AMOC-AMO relationship is supported by several lines of evidence, but continues to be an area of active research.